https://onlinelibrary.wiley.com/doi/pdf/10.1002/inf2.12028

Machine learning in materials science

A novel gas turbine fault diagnosis method based on transfer learning with CNN

Intelligent welding system technologies: State-of-the-art review and perspectives

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

/pdf/a-review-on-deep-learning-applications-in-prognostics-and-3cw1pwlic5.pdf

A Review on Deep Learning Applications in Prognostics and Health Management

Deep learning is revolutionising the way that many industries operate, providing a powerful method to interpret large quantities of data automatically and relatively quickly. Deterioration is often multi-factorial and difficult to model deterministically due to limits in measurability, or unknown variables. Deploying deep learning tools to the field of materials degradation should be a natural fit. In this paper, we review the current research into deep learning for detection, modelling and planning for material deterioration. Driving such research are factors such as budget reductions, increasing safety and increasing detection reliability. Based on the available literature, researchers are making headway, but several challenges remain, not least of which is the development of large training data sets and the computational intensity of many of these deep learning models.

/pdf/a-review-of-deep-learning-in-the-study-of-materials-3roz2icvdb.pdf

A review of deep learning in the study of materials degradation

In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

https://iopscience.iop.org/article/10.1088/1742-6596/933/1/012006/pdf

Automatic Detection of Welding Defects using Deep Neural Network

Deep features based on a DCNN model for classifying imbalanced weld flaw types

The invention discloses a forging press machine operation condition information acquisition and analysis system based on the internet of things technology. According to the system, the operation condition analog quantity is obtained through a sensor; the forging press machine operation condition switching values are obtained through acquiring the PLC signal points; the information contents are processed by a data acquisition terminal interface circuit and then transmitted to processor chips of the data acquisition terminal; then the information contents are transmitted to a cloud server through a GPRS (General Packet Radio Service) communication module for the convenience of data sharing. By means of the server built-in software, the cloud data is analyzed in real time; on one hand, machine tool users can obtain the machine tool operation reports or pre-warning information through PC so that they can monitor the machine tool condition at any time, and obtain fault diagnosis reports from machine tool seller technical staff; on the other hand, the seller technical staff can inquire the machine tool operational efficiency and working curves through PC at any time to acquire the machine tool physical condition in real time and to estimate faults in time.

Forging press machine operation condition information acquisition and analysis system based on the internet of things technology

This paper proposed a novel in-plane displacement field measurement algorithm using an optical flow strategy. We built a linear illumination model between images before and after deformation to guarantee intensity invariability. We used image upsampling and a reliability-guided strategy to find the matching points accurate to 0.5 pixels in the reference and deformed images. The criterion to determine the reliability is zero-mean normalized cross-correlation coefficient. Afterward, we used the brightness constancy constraint combined with Lucas-Kanade optical flow constraint in a specific image region to obtain an overdetermined linear equation. We applied the noniterative least-squares algorithm to solve the equations and to achieve the displacement offsets. This research utilized multithread calculation to handle the complete cracking applications. We estimated the computing efficiency and calculation precision of the proposed method through a series of experimental speckle patterns. All results demonstrated the correctness, effectiveness, and robustness of the proposed method.

Ye Wei

Papers

Automatic Detection of Welding Defects using Deep Neural Network

Deep features based on a DCNN model for classifying imbalanced weld flaw types

Forging press machine operation condition information acquisition and analysis system based on the internet of things technology

Technique for two-dimensional displacement field determination using a reliability-guided spatial-gradient-based digital image correlation algorithm.

Bearing Fault Diagnosis Using Structure Optimized Deep Convolutional Neural Network under Noisy Environment